To analyze the massive amount of data, many data analysis tools are available that efficiently delivers useful data. Here, we have included top 10 best data analysis tools.
What is Data Analysis?
The data collected through different resources apply some methods over it for meaningful data called data analysis. And these processes are cleaning, transforming, and modeling. After analysis, available useful information to take the precise decision.
Types of Data Analysis:
There are two kinds of data analysis.
- Qualitative Analysis – In this process, collect the data through interviews and observations.
- Quantitative Analysis – The data which is collected through surveys and experiments is a quantitative analysis.
Best Data Analysis Tools
LaptopTheory have shortlisted some best data analysis tools, and these are:
- Microsoft Excel
- Ervin DM
- Apache Spark
- R Programming
- Splice Machine
Microsoft Office is a well-known application that is developed by Microsoft. It is the part of MS Office. Microsoft developed it for all devices such as windows, Mac OS, Android, and IOS.
It’s a robust and reliable program that is used to store data. After data collection using its feature and formulas, you can do analysis and generate documentation. Microsoft Excel uses spreadsheets and featured so many functions that make work easier and enhance throughput. Data management, charting and graphing, financial modeling, programming, etc., are the primary uses of Microsoft Excel.
Moreover, a pivot table is more reliable to collect the data in a spreadsheet. It compares the already stored information and avoids duplications. To simplify your data in Microsoft Excel is too much easy due to its filtering featuring.
This is a business analytical tool that focused on self-services. It’s a dashboard platform that helps to monitor, analyze and create a beneficial report. This powerful tool focused on simple analysis features that are suitable for beginner and advanced level users. Datapine is based on artificial intelligence and machine learning that offers next-level data analytics features such as predictive analytics.
This advanced-level data analytics software can integrate numerous data sources with a few clicks. And those data sources such as ERP, databases, CRM, social media data, etc.
It’s a collaborative data analysis and graphing tool. This, one of the best data analysis tools allow you some impressive features like the user can import data from any format, analyze and simulate, graph the data, privately share and edit your data.
Simply we can say that Plotly is a platform for publishing interactive graphs from Python to the web.
Rapidminer is data science software that is more helpful for data analysts and data scientists. It can perform so many functions such as data preparation, deep learning, text mining, machine learning, and predictive model development. As well as data analysts and data scientists enhance productivity due to its machine learning feature. And this is all possible due to more than 1500 algorithms and data functions.
Moreover, integration with Python and R is possible. Lastly, if you are searching for the best software for deep learning and data sciences, RapidMiner is one of them.
It’s a statistical analysis system used for data management, business intelligence, advanced analytics, predictive analytics, etc. As well as it enables the programmers to uses this for so many purposes such as:
- Information retrieval and data management
- Report writing and graphics
- Data mining
- Business planning
- Project management
- Quality improvement
Apart from this, SAS permits the users to produce a massive amount of forecasts and automate their processes. Moreover, it has a lot of features that make life easier, like easy to use GUI.
When we shortlist the best data analysis tools, then without data modeling software list is incomplete. Here in this list, Ervin DM is the best data modeling software that completes the data analysts list.
This advanced-level data modeling tool for visualizing metadata and database schema to understand complex data sources. It reduces and the complexity and makes it easier to understand. Apart from this, to uses the Ervin DM tool, users experience so many impressive experiences such as:
- Virtualization of data from anywhere
- Automated data model and database schema
- Centralized mode development and management
- Data literacy, collaboration, and accountability
- Agile application development
- Increase data quality
- Reduce risk and cost
- Successful cloud adoption
It’s a unified data analysis engine that is suitable for massive data management. In a robust data environment, it provides a quality data analysis process with high performance. Besides, a large ecosystem of data frame rates, streaming, machine learning, and graph computation is the heightened feature of apache spark.
Apache spark is a free and open-source tool, and the programmer uses its ease APIs in own apps to analyze a large amount of data. Furthermore, its ecosystem consists of spark SQL, machine learning, core java, python APIs, etc., to ease the development.
It’s a language and environment for statistical computing, graphics, and data analysis. R programming is a useful language that includes conditions, loops, user-defined functions, etc. It provides linear and nonlinear modeling techniques and can be used on Windows, Mac, and UNIX OS.
R programming is free software that can perform some operations such as statistical analysis, data mining, and data analysis. As well, it able to handle and store a massive amount of data. It is useful to many users, such as data scientists, data visualization, polls and survey, and database studies.
It’s a next-level database management system that makes that easier to connects the data center and cloud. Splice Machine is the only SQL scale-out relation database management system with built-in native machine learning.
Some company claims, using splice machine build the smarter predictive applications that can integrate the fast data streaming, analytics, and machine learning, heavy workload, etc.
It’s a programming language that uses technical data analysts and data scientists. Python is an open-source that can handle different analyses and allows it to integrate with other available packages like machine learning and data visualization.
Python is a highly portable language, which means a user can run this on a different OS without any code changes. It is useable on both OS mac and windows. Furthermore, to advanced data analysis processes, its data mining and machine learning features make the best of Python.
Above are the top-of-the-line and the best data analysis tools that help to analyze a massive amount of data. These shortlisted tools include data mining tools, statistical analysis tools, database management tools. Before choosing the best data analysis tools, deeply understand your work and then make your selection accordingly.
R programing is useful for data sciences due to its features that provide impressive results. Besides, RapidMiner is another best tool for a data scientist that helps to provide an excellent throughput. As well as it’s an excellent platform for text mining, machine learning, etc.